This 2-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.
At the end of this course, participants will be able to:
• Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform
• Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform
• Employ BigQuery and Cloud Datalab to carry out interactive data analysis
• Choose between Cloud SQL, BigTable and Datastore
• Train and use a neural network using TensorFlow
• Choose between different data processing products on the Google Cloud Platform
Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following:
• A common query language such as SQL
• Extract, transform, load activities
• Data modeling
• Machine learning and/or statistics
• Programming in Python
Google Account Notes:
• Google services are currently unavailable in China.

AR

This course gives you a pretty good fundamental idea on what you are diving into. Google cloud platform has bucket of data engineering features. This course sets you on the starting line for them.

CR

Dec 27, 2017

Filled StarFilled StarFilled StarFilled StarFilled Star

This was a great course to understand at a high level how to design and create my data ecosystem and how to do it sustainably. Hopefully, next courses provide more in-depth the technical features.

수업에서

Recommending Products using Cloud SQL and Spark

In this module you will have an existing Apache SparkML recommendation model that is running on-premise. You will learn about recommendation models and how you can run them in the cloud with Cloud Dataproc and Cloud SQL.

강사:

Google Cloud Training

스크립트

It's now time for your lab. You are in charge of migrating your company's existing machine learning workload for housing recommendations from your on-premises Hadoop cluster to the Cloud. Your organization is happy with the current model, but the underlying infrastructure on-premise is causing them headaches to tune and utilize efficiently. Your CTO wants as little friction as possible from your existing Hadoop on-premise infrastructure, but has heard of the advantages that Cloud solutions offer for autoscaling and serverless management. So here are your lab objectives. Create a Cloud SQL instance and populate the tables. Explore the rentals data using SQL statements using Cloud Shell. Launch Dataproc. Train and apply a machine learning model written in PySpark to create product recommendations. Explore the inserted rows in Cloud SQL. Here's what the setup will look like. In step one, we set up Google Cloud Storage and Cloud SQL. And then import the records from GCS into Cloud SQL. And now that your ratings are in Cloud SQL, in step two, you will run a machine learning training job in Cloud Dataproc to read those ratings and train the machine learning model. In step three, you will run the model in Cloud Dataproc to create recommendations. And save the top five recommendations for each user back into Cloud SQL. Lastly, step four, your ratings can be delivered back to users through App Engine. You won't do steps one and four in this lab because those steps deal mostly with web programming. In this lab, you concentrate on doing steps two and three. Try out the lab, and keep in mind that you have multiple attempts for each lab and you can always come back and practice more.